performance

Notion’s 200-Billion-Note Machine, Explained in 2 Minutes Every paragraph, checkbox, or emoji in Notion is a “block” row in PostgreSQL. When that single DB hit 20 billion blocks, latency spiked and index bloat set in. Sharding by workspace_id, Notion got 480 logical shards spread across 96 Postgres instances, all routed with a simple hash(workspace_id) % 480. Find the key lessons about choosing boring tech stack and attitude towards observability. #performance #observability

How Notion uses horizontal scalability and sharding to handle 200+ billion notes without crashing.

1000 Models at Instagram Instagram definitely runs an ML model to provide you with recommendations, but there is a thousand more cases when models are involved like rating comments, suggesting tagged people and others. Running this number of models gets you challenged from the quality, speed and underlying infrastructure perspective. Luckily, instagram engineers share their journey. #ml #performance #scalability

In this post, we explore how Instagram has successfully scaled its algorithm to include over 1000 ML models without sacrificing recommendation quality or reliability.  We delve into the intricacies…

Premature optimization “Premature optimization is a root of all evil”, right? Optmizations are actually about doing 3 “T” properly: a right thing at a right time and about the right trade-offs. Alex Ewerlöf wrote a new great piece of advise that I found incredibly relevant, at least my former experience proofs so.

Replacing a cache services with a database A prominent idea seen on x and bluesky is throwing away cache in favor of a read replica. In the end databases cache some data in memory, why not leverage it? Well, there are counter-arguments to this approach and you want to know them. #performance #caching

Why do we use caches at all? Can databases fully replace them?